One of the main shortcoming in using RBM-based models, such as DBM and DBN, concerns their fine-tuning hyperparameter task, which aims at selecting a suitable set of parameters in such a way that the reconstruction error is minimized. In this work, we considered IHS, FA, CS, AIWPSO, BSA, JADE, and the CoBiDE against RS for DBM hyperparameter fine-tuning. We also evaluated the robustness of the proposed approach using three distinct DBN and DBM models: one layer (1L), two layers (2L) and three layers (3L). Finally, Table REF  presents the parameters used for each optimization techniqueParameters were empirically selected based on each technique author's suggestions, as well as the values commonly adopted in the literature [1]}, [2]}, [3]}, [4]}, [5]}, where 5 agents (initial solutions) were used for all optimization techniques during 50 iterations for convergence The selected number of agents and iterations for convergence were empirically chosen based on values commonly adopted in the literature [1]}, [2]}, [3]}..
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